Survey of Marine Vessel Operators

Detailed information for 2023

Statistics Canada is conducting this survey in collaboration with Transportation Canada to produce statistical information on marine vessel operators in Canada.

Data release - May 16, 2025

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

This survey collects information on commercial water carriers operating in Canada including financial and operational data about the type of vessel (passengers or commodities), vessel ownership, charters, sources of power and maintenance oil, number of employees, and freight type transported. The results of this survey will help governments and industry better understand and assess the impacts of the Canadian-based marine transportation service providers on the economy, supply chain, and the sector's competitiveness on an on-going basis. This survey will evaluate the nature of business of marine vessel operators across the country and address the need to fill in the gaps in data regarding water transportation and non-transportation activities.

Reference period: Calendar year

Subjects

• Transportation
• Transportation by water

Data sources and methodology

Target population

The target population includes all Canadian-domiciled business entities legally registered in Canada involved in water transportation or non-transportation activities in Canadian waters and that own or charter vessels. Off-shore (foreign) marine vessels are included if the revenues raised from such activities are reported by the Canadian-domiciled entities.

Establishments of interest are those whose primary activity is marine transport and are defined in three sub-sectors of North American Industry Classification System (NAICS) including water transportation (483), scenic and sightseeing transportation, water (4872), and support activities for water transportation (4883), or businesses listed on the Canadian vessel registry.

Excluded from the target population are companies that operate private pleasure craft, fishing boats, defence vessels.

Instrument design

The collection instrument for this survey is an electronic questionnaire. The questionnaire is the result of collective input from stakeholders both internal and external to Statistics Canada.

Respondents are mailed or e-mailed a secure access code to respond to the electronic questionnaire.

Sampling

This is a sample survey with a cross-sectional design.

Sampling unit
The sampling unit for this survey is an establishment.

Stratification method
Units are stratified by four industry groups, where each industrial group is divided into two size groups, based on the revenue, at the establishment level.

Additionally, two strata were built, containing a total of 87 establishments that were linked to Transport Canada's Vessel Registry, where all establishments within them were selected in the sample with certainty.

Sampling and sub-sampling
The total sample size was set at 1,580 units, and units were allocated to ensure similar quality across industry groups and balance among the estimation domains. The "large size" strata were sampled with certainty while the remaining sample was allocated to the "small size" strata in order to meet the required precision when estimating totals at the industry group level.

Data sources

Data collection for this reference period: 2024-11-04 to 2025-02-03

Responding to this survey is mandatory.
Data are collected directly from survey respondents.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Error detection is an integral part of data processing activities. Prior to imputation, a series of edits are applied to the collected data to identify errors and inconsistencies. Errors and inconsistencies in the data are reviewed and resolved by referring to data for similar units in the survey and information from external sources. If a record cannot be resolved, it is flagged for imputation. Finally, edit rules are incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data are consistent.

Imputation

After microdata verification, a variable was created for each of the survey variables to identify those that had either failed the verification rules or had missing values. Imputation was performed to reduce the amount of missing, inconsistent or incomplete data. The missing data were usually imputed using a randomly selected donor inside the imputation class. These imputation classes were formed based on statistical analysis performed with frame information or previous variables on the questionnaire.

For donor imputation, a minimum number of units was required within each imputation class. When imputation classes were too small, larger classes were created by combining several classes together.

Estimation

Estimation is a process by which Statistics Canada obtains values for the population of interest so that it can draw conclusions about that population based on information gathered from only a sample of the population. For this survey, the sample used for estimation comes from a single-phase sampling process.

An initial sampling weight (design weight) is calculated for each unit of the survey and is simply the inverse of the probability of selection. The weight calculated for each sampling unit indicates how many other units it represents.

However, since some of the selected units did not answer the survey, reweighting is performed on the responding units so that their final weights still represent the whole target population. The response mechanism can be considered as a second phase of the sampling process.

After the reweighting is performed, a calibration process is performed so that the weighted totals per calibration groups equal the population totals.

Estimation of the totals of a numerical variable is done by multiplying the variable values by their respective calibrated weight. Estimation of proportions is done using the calibrated weights to calculate the population totals in the domains of interest.

Quality evaluation

Estimates were reviewed to ensure that the findings are logical and quality checks were carried out to ensure that estimates are consistent. Atypical results were flagged for investigation and were corrected as necessary.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Revisions and seasonal adjustment

This methodology does not apply.

Data accuracy

There are two types of errors which can impact the data: sampling errors and non-sampling errors.

Estimates are subject to sampling error. This error is expressed either as a standard error (SE) or a coefficient of variation (CV). The following rules based on the standard error (SE) are used to assign a measure of quality to all of the estimates of percentages (expressed as a percentage):
A = Excellent (0.00% to less than 2.50%)
B = Very good (2.50% to less than 5.00%)
C = Good (5.00% to less than 7.50%)
D = Acceptable (7.50% to less than 10.00%)
E = Use with caution (10.00% to less than 15.00%)
F = Too unreliable to be published (Greater than or equal to 15%, data are suppressed)

The following rules based on the coefficient of variation (CV) are used to assign a measure of quality to all of the estimates of counts and totals:
A = Excellent (0.00% to less than or equal to 5.00%)
B = Very good (5.01% to less than or equal to 10.00%)
C = Good (10.01% to less than or equal to 15.00%)
D = Acceptable (15.01% to less than or equal to 25.00%)
E = Use with caution (25.01% to less than or equal to 35.00%)
F = Too unreliable to be published (Greater than 35.00%, data are suppressed)

Non-sampling errors may occur for various reasons during the collection and processing of the data. For example, non-response is an important source of non-sampling error. Under or over-coverage of the population, differences in the interpretations of questions and mistakes in recording and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire and verification of the survey data.

The overall response rate for 2023 was 79.7%.